How animals grow and how they should be fed so as to maximize enterprise economics and manage accelerated animal raising environmental pollution; are questions animal scientists have attempted to address for a long time. Further, due to genetic improvements, changes in living environments and economic factors, the animals are not necessarily fed to reach the same body size or have the same growth rate from one time period to another. In view of these complexities, it is challenging to meet both market requirements and maximize the business margins at various genetic and environmental conditions. One method, considered by many to be the best way to solve the foregoing problems is nutrition modeling. Such modeling is attractive due to its nature of describing the relationship between nutrients and animal growth under different genetic and environmental conditions.
Those skilled in the art will appreciate that the efficiency of nutrient utilization (i.e., the proportion of digestible nutrient that can be biologically utilized by an animal for tissue synthesis and other metabolic functions) is one of the most important factors influencing growth model accuracy. However, due to variations in results from experiment to experiment, as well as the many approaches in interpreting the results, a single nutrient efficiency value of a dietary amino acid often has a broad range. In one study, for example, the efficiency value of a dietary amino acid for body tissue protein deposition of poultry ranged from 75% to 85%. These large discrepancies in nutrient efficiency rates contributes to large secondary errors in animal growth model construction. Rigid (singular) nutrient efficiency values have been used for model construction in the past (e.g., Fisher, C. 1983, The physiological basis of the amino acid requirements of poultry. In: Protein Metabolism and Nutrition (editors M. Arnal, R. Pion and D. Bouin), Proc IV Int. Sym., Clermont-Ferrand. Vol. 1, pp. 385-404. Les colloques de l'INRA, No. 16; and Talpaz H., J. R. de la Torre, P. J. H. Sharpe, and S. Hurwitz, 1986, "Dynamic optimization model for feeding Boilers," Agricultural Systems, 20:121-132).
One of the major reasons for the large differences in nutrient efficiency value is that animals are fed on a population basis. Thus, each individual animal has its own growth potential and its own nutrient requirement to meet that potential. When these diverse individual animals are assembled together in a flock, herd, school of fishes, etc., the resultant population average of nutrient efficiency of utilization depends on the intra-population variation of individual animal nutrient requirements and associated dietary nutrient level of a tested population. The results of these tests were that higher dietary nutrient levels fed to animals resulted in lower nutrient efficiency. The reason for the lower efficiency was that the nutrient requirement for a larger proportion of animals in the population was met, and only the higher nutrient requiring animals use the extra nutrient for production.
The Reading Model (see, Fisher, C., T. R. Morris, and R. C. Jennings, 1973, "A model for the description and prediction of the response of laying hens to amino acid intake," British Poultry Science, 14:469-484) describes the animal nutrient requirement based on a population variation. It was originally used in the description of egg production for laying hens. The essential feature of this approach is to look at nutrient response of each bird independently and then to derive the population response as an integration of each individual bird response. The "optimum" flock requirement of each nutrient can be calculated through this approach by knowing the unit cost of this individual nutrient and value of unit output product.
Although the Reading Model approach is useful in some applications, it has several drawbacks as indicated herein:
1. The approach calculates optimum requirement of each nutrient independently. Therefore, nutrient balance and interactions among nutrients are ignored. PA1 2. The cost of each calculated nutrient is required to use this model for enterprise economics. This can be a severe obstacle due to the fact that nutrient cost is mostly associated with each ingredient (i.e., each ingredient contains many nutrients). The final nutrient cost depends on the final ingredient composition of the diet due to nutrient competition among the available ingredients to meet minimum nutrient constraints during an optimization process. PA1 3. The calculation of optimum nutrient requirement of a population is based on the economic break point of nutrient cost and value of product in the Reading Model. This may not be true due to the commercial and financial integration of multiple "divisions" within a modern enterprise. Optimum nutrient level can be higher or lower than the one at the economic break point due to higher or lower overhead costs such as costs of processing, labor, production, multiple value added products derived from a single farm product, etc.
Therefore, there arises an industrial need for a method, process and an apparatus for determining the optimum utilization effectiveness of nutrients for a population of animals. Additionally, the method and apparatus preferably should be capable of being used in combination with an apparatus and method for generating animal growth alternatives. The present invention directly addresses and overcomes the shortcomings of the prior art.